What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process.When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses.We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.

The purpose of this study was to describe cross-country differences with respect to the reasons for dental non-attendance by Europeans currently aged 50 yr and older. The analyses were based on retrospective life-history data from the Survey of Health, Ageing and Retirement in Europe and included information about various reasons why respondents from 13 European countries had never had regular dental visits in their lifetimes. A series of logistic regression models was estimated to identify reasons for dental non-attendance across different welfare state regimes. The highest percentage of respondents without any regular dental attendance throughout their lifetimes was found for the Southern welfare state regime, followed by the Eastern, the Bismarckian, and the Scandinavian welfare state regimes. Factors such as patients’ perception that regular dental treatment is ‘not necessary’ or ‘not usual’ appear to be the predominant reason for non-attendance in all welfare state regimes. Within the Southern, Eastern, and Bismarckian welfare state regimes, the health system level factor ‘no place to receive this type of care close to home’ and the perception of regular dental treatment as ‘not necessary’ were more often referred to than in Scandinavia. This could be relevant information for health care decision makers in order to prioritize interventions towards increasing rates of regular dental attendance.

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Clinical treatment planning situations arise which require different wedge angles within segments of a single therapeutic x‐ray field. Idealized wedge‐shaped dose distributions, including combination of several wedge segments of different angle within a single field, are generated and delivered through computer control of asymmetric collimator motion and dose per field segment. A dual‐pass technique is introduced to provide improved adherence to the prescribed isodose distribution. Dynamic wedge distributions are verified by film densitometry and ionization chamber measurement. These results suggest the potential importance of this technique as an added clinical radiotherapy tool.

Objective. To compare estimates of dental visits among adults using three national surveys.